@inproceedings{chen-etal-2026-adapt,
title = "{ADAPT}: Benchmarking Commonsense Planning under Unspecified Affordance Constraints",
author = "Chen, Pei-An and
Liang, Yongching and
Yeh, Jia-Fong and
Su, Hung-Ting and
Chen, Yi-Ting and
Sun, Min and
Hsu, Winston H.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1109/",
pages = "24188--24206",
ISBN = "979-8-89176-390-6",
abstract = "Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. However, existing methods usually focus on directly executing instructions, without considering whether the target objects can actually be manipulated, meaning they fail to assess available affordances. To address this limitation, we introduce DynAfford, a benchmark that evaluates embodied agents in dynamic environments where object affordances may change over time and are not specified in the instruction. DynAfford requires agents to perceive object states, infer implicit preconditions, and adapt their actions accordingly. To enable this capability, we introduce ADAPT (Affordance-Driven Adaptive Planning and Task execution), a plug-and-play module that augments existing planners with explicit affordance reasoning. Experiments demonstrate that incorporating ADAPT significantly improves robustness and task success across both seen and unseen environments. We also show that a domain-adapted, LoRA-finetuned vision-language model used as the affordance inference backend outperforms a commercial LLM (GPT-4o), highlighting the importance of task-aligned affordance grounding."
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<abstract>Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. However, existing methods usually focus on directly executing instructions, without considering whether the target objects can actually be manipulated, meaning they fail to assess available affordances. To address this limitation, we introduce DynAfford, a benchmark that evaluates embodied agents in dynamic environments where object affordances may change over time and are not specified in the instruction. DynAfford requires agents to perceive object states, infer implicit preconditions, and adapt their actions accordingly. To enable this capability, we introduce ADAPT (Affordance-Driven Adaptive Planning and Task execution), a plug-and-play module that augments existing planners with explicit affordance reasoning. Experiments demonstrate that incorporating ADAPT significantly improves robustness and task success across both seen and unseen environments. We also show that a domain-adapted, LoRA-finetuned vision-language model used as the affordance inference backend outperforms a commercial LLM (GPT-4o), highlighting the importance of task-aligned affordance grounding.</abstract>
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%0 Conference Proceedings
%T ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
%A Chen, Pei-An
%A Liang, Yongching
%A Yeh, Jia-Fong
%A Su, Hung-Ting
%A Chen, Yi-Ting
%A Sun, Min
%A Hsu, Winston H.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F chen-etal-2026-adapt
%X Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. However, existing methods usually focus on directly executing instructions, without considering whether the target objects can actually be manipulated, meaning they fail to assess available affordances. To address this limitation, we introduce DynAfford, a benchmark that evaluates embodied agents in dynamic environments where object affordances may change over time and are not specified in the instruction. DynAfford requires agents to perceive object states, infer implicit preconditions, and adapt their actions accordingly. To enable this capability, we introduce ADAPT (Affordance-Driven Adaptive Planning and Task execution), a plug-and-play module that augments existing planners with explicit affordance reasoning. Experiments demonstrate that incorporating ADAPT significantly improves robustness and task success across both seen and unseen environments. We also show that a domain-adapted, LoRA-finetuned vision-language model used as the affordance inference backend outperforms a commercial LLM (GPT-4o), highlighting the importance of task-aligned affordance grounding.
%U https://aclanthology.org/2026.acl-long.1109/
%P 24188-24206
Markdown (Informal)
[ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints](https://aclanthology.org/2026.acl-long.1109/) (Chen et al., ACL 2026)
ACL
- Pei-An Chen, Yongching Liang, Jia-Fong Yeh, Hung-Ting Su, Yi-Ting Chen, Min Sun, and Winston H. Hsu. 2026. ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 24188–24206, San Diego, California, United States. Association for Computational Linguistics.